Automated Sensor Performance Evaluation of Robot-Guided Vehicles for High Dynamic Tests

As the demand for automated vehicle testing on proving grounds grows, the need for comprehensive and reliable environment monitoring systems becomes increasingly important. In highly dynamic driving test scenarios, long-range perception is essential for detecting dangers and hazards, ensuring the safety of both the test vehicle and other people on the track. However, determining an appropriate sensor setup can be challenging due to the complexity of sensor perception limitations. Perception limitations depend on the sensor characteristics and the environment. In this work, we propose a new approach to automatically evaluate sensor performance for high dynamic driving to improve the safety and efficiency of automated testing on proving grounds. Our approach involves estimating the detection range of common sensor technologies and analyzing the performance of sensor systems under various environmental conditions. By evaluating sensor performance in advance and comparing different sensor setups on tracks with a high-speed profile, we are able to identify critical track sections with higher collision risks and safeguard tests accordingly. This study emphasizes the importance of advanced environmental monitoring and sensor analysis in ensuring the safety and efficiency of automated vehicle testing.